A memory consolidation state evaluation method, system, computer device and medium

Abstract

This invention provides a method, system, computer equipment, and medium for assessing memory consolidation, belonging to the field of magnetic resonance imaging (MRI) technology. The method includes five stages: memory preparation, memory baseline, memory monitoring, memory retrieval, and memory stabilization. After the memory encoding task is completed, MRI data with a common time reference are acquired at multiple preset monitoring time points using alternating temporal sequences of MRI, resting-state oxygenation level-dependent imaging, and MRI. Functional image sequences and MRI data are reconstructed through frequency domain transformation to extract functional connectivity strength values ​​between preset brain regions of interest and the hippocampus, while simultaneously calculating the excitation / inhibition balance ratio. The functional connectivity strength values ​​and the excitation / inhibition ratio are fitted along a time axis to generate a coordinated change trajectory, thereby outputting the memory consolidation assessment result. This invention achieves synchronous dynamic assessment of neurochemistry and brain network state during memory consolidation.

Description

Technical Field

[0001] This invention belongs to the field of magnetic resonance imaging technology, specifically relating to a method, system, computer equipment, and medium for assessing memory consolidation status. Background Technology

[0002] In the fields of cognitive neuroscience and clinical neuromedicine, assessing the brain's memory mechanisms is crucial for understanding normal cognitive function and identifying early cognitive impairments. In the early stages of neurodegenerative diseases such as Alzheimer's disease and mild cognitive impairment, patients often exhibit decreased memory encoding ability and impaired memory consolidation. However, current clinical assessment methods largely rely on neuropsychological scales and behavioral tests, making it difficult to in vivo and non-invasively detect abnormalities in the neurochemical and functional networks of memory-related brain regions. Therefore, developing technologies that can simultaneously reflect neural activity patterns and neurotransmitter metabolic states has significant clinical application value for the early identification and intervention monitoring of memory-related diseases.

[0003] While functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRI) have been applied to memory mechanism research, their data acquisition lacks synergistic optimization for the memory process in terms of temporal design. Specifically, existing methods do not establish an alternating acquisition protocol for dynamic monitoring of neurotransmitters after memory encoding and assessment of brain functional connectivity, making it difficult to establish a temporal correlation between changes in neurotransmitter concentration and the evolution of brain functional connectivity. Furthermore, existing technologies for monitoring changes in memory-related neurotransmitters are mostly limited to cross-sectional comparisons at baseline levels, lacking longitudinal tracking methods for the evolution of neurotransmitter concentration after memory encoding. Because the dynamic response of neurotransmitters and the remodeling of brain functional connectivity involve complex interactions in the memory consolidation process, data acquisition from a single modality or a single time point is insufficient to provide temporal evidence of their synergistic changes. Summary of the Invention

[0004] To address the aforementioned background problems, this invention provides a method, system, computer device, and medium for assessing memory consolidation status. Through optimized data acquisition timing design, it can simultaneously acquire neurochemical data and brain functional connectivity data with a common time reference during continuous monitoring of the memory consolidation process, providing a data foundation for analyzing the synergistic evolution of neurotransmitters and brain network states.

[0005] To achieve the above objectives, the present invention provides a method for assessing memory consolidation status, comprising: The system acquires resting oxygen level-dependent imaging data and magnetic resonance spectroscopy data at multiple preset monitoring time points during the memory consolidation process of the target object after the completion of the memory encoding task; the resting oxygen level-dependent imaging data and magnetic resonance spectroscopy data are acquired at different preset monitoring time points.

[0006] Frequency domain transformation was performed on resting-state blood oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. Functional connectivity strength values ​​between a preset brain region of interest and the hippocampus were extracted from the functional image sequence. These functional connectivity strength values ​​characterize the brain functional network coordination state of the target subject during memory consolidation. Frequency domain transformation was also performed on magnetic resonance spectroscopy (MRS) imaging data to obtain MRS data. Glutamate and γ-aminobutyric acid (GABA) concentrations were extracted from the MRS data using the LCModel fitting algorithm. Based on these glutamate and GABA concentrations, the excitability-to-inhibitory ratio of the target subject was determined. This excitability-to-inhibitory ratio characterizes the neurotransmitter metabolic balance state of the target subject in the preset brain region of interest during memory consolidation.

[0007] The functional connectivity strength value and the ratio of excitability to inhibition at multiple preset monitoring time points are curve-fitted along the time axis to generate a trajectory that characterizes the changes in the neurotransmitter metabolic balance and brain functional network coordination state of the target object during the memory consolidation process.

[0008] Preferably, the resting-state oxygenation level-dependent imaging data is acquired based on a resting-state oxygenation level-dependent imaging pulse sequence, and the magnetic resonance spectroscopy imaging data is acquired based on a magnetic resonance spectroscopy imaging pulse sequence.

[0009] Preferably, the magnetic resonance spectroscopy imaging is multi-phase magnetic resonance spectroscopy imaging, and the number of phases of the multi-phase magnetic resonance spectroscopy imaging is 3 to 6; the multiple preset monitoring time points include the baseline state before the memory coding task and the time points at 5 minutes, 20 minutes, 25 minutes, 30 minutes and 24 hours after the completion of the memory coding task.

[0010] Preferably, the magnetic resonance spectroscopy imaging pulse sequence includes a short echo time sequence and a J-edit sequence; the glutamate concentration is quantified by the short echo time sequence, and the γ-aminobutyric acid concentration is quantified by the J-edit sequence.

[0011] Preferably, the preset brain region of interest includes the medial prefrontal cortex.

[0012] Preferably, the slope characteristics of the change trajectory include positive slope, negative slope, and zero slope, which respectively indicate that at the corresponding preset monitoring time point, the functional connection strength value or the ratio of excitability to inhibition shows an upward trend, a downward trend, and a stable trend.

[0013] Preferably, the calculation of the functional connectivity strength value includes: extracting the Bold time signal sequence of a preset brain region of interest and the Bold time signal sequence of the hippocampus brain region, calculating the Pearson correlation coefficient between the two sequences, and using the correlation coefficient as the functional connectivity strength value.

[0014] The present invention also provides a memory consolidation status assessment system, comprising: The data acquisition module is used to acquire resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data of the target object at multiple preset monitoring time points during the memory consolidation process after the completion of the memory encoding task; the resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data are acquired at different preset monitoring time points.

[0015] The computational module performs frequency domain transformation on resting-state blood oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. It extracts functional connectivity strength values ​​between a preset brain region of interest and the hippocampus from the functional image sequence; these values ​​characterize the brain functional network coordination state of the target subject during memory consolidation. The module also performs frequency domain transformation on magnetic resonance spectroscopy (MRS) imaging data to obtain MRS data. Using the LCModel fitting algorithm, it extracts glutamate and γ-aminobutyric acid (GABA) concentrations from the MRS data. Based on these concentrations, it determines the excitability-inhibition ratio of the target subject; this ratio characterizes the neurotransmitter metabolic balance state of the target subject in the preset brain region of interest during memory consolidation.

[0016] The trajectory generation module is used to perform curve fitting on the functional connectivity strength value and the ratio of excitability to inhibition at multiple preset monitoring time points along the time axis to generate a trajectory that characterizes the changes in the neurotransmitter metabolic balance and brain functional network coordination state of the target object during the memory consolidation process.

[0017] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the memory consolidation state assessment method.

[0018] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, is capable of executing any of the steps in the memory consolidation state assessment method.

[0019] The memory consolidation state assessment method provided by this invention has the following beneficial effects: Within the critical time window of memory consolidation, this invention comprehensively monitors the immediate neurotransmitter response data, intermediate functional connectivity data, and subsequent neurotransmitter evolution data during memory consolidation by collecting resting-state oxygenation level-dependent imaging data and magnetic resonance spectroscopy data at different time points. These data have a strict temporal correspondence, thus providing a data foundation for analyzing the synergistic changes in neurochemistry and functional networks. Furthermore, this invention extracts functional connectivity strength values ​​from resting-state oxygenation level-dependent imaging data with a common time reference, and extracts the concentrations of glutamate and γ-aminobutyric acid from magnetic resonance spectroscopy data through LCModel fitting to calculate the excitatory-inhibitory E / I ratio, thereby obtaining quantitative parameters for joint trajectory fitting. Both the functional connectivity strength value and the E / I ratio are quantitative and calculable parameters that can reflect state changes in the memory consolidation process from the brain network level and the neurotransmitter metabolism level, respectively. This generates a synergistic trajectory of changes in functional connectivity strength value and E / I ratio over time. This trajectory correlates the dynamic remodeling of brain functional networks (characterized by functional connectivity strength) with changes in neurotransmitter metabolism in local brain regions (characterized by the E / I ratio) within a unified temporal framework, directly reflecting the dynamic coupling process between the excitation / inhibition balance in a specific brain region and the functional connectivity strength in distant brain regions during memory consolidation. Therefore, this invention provides a more accurate and informative imaging assessment basis for subsequent cognitive state research. Attached Figure Description

[0020] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a memory consolidation state assessment method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating an embodiment of the present invention; Figure 3 This is a schematic diagram of sequence acquisition according to an embodiment of the present invention; Figure 4 The images shown are sagittal and coronal views of Embodiment 1 of the present invention, along with the selected locations of the monomers. Figure 4 (a) is in the sagittal position; Figure 4 (b) is the coronal position; Figure 5 This is a conventional ¹H MRS spectrum of Example 1 of the present invention; Figure 6The J-edit¹H MRS spectrum of Embodiment 1 of the present invention; Figure 7 This is a neurotransmitter change curve from Example 1 of the present invention; Figure 8 This refers to the task-state activation differences and resting-state brain functional connectivity changes in Embodiment 1 of the present invention; wherein, Figure 8 (a) shows that in Example 1 of the present invention, memory retrieval after 30 minutes of memory consolidation across the night significantly activated brain regions under the condition of old image compared to new image; Figure 8 (b) Overnight retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation under similar conditions compared to new images; Figure 9 This is a graph showing the changes in resting-state brain functional connectivity in Embodiment 1 of the present invention. Figure 10 The sagittal and coronal views of Embodiment 2 of the present invention and the selected locations of the monomers are shown; wherein, Figure 10 (a) is in the sagittal position; Figure 10 (b) is the coronal position; Figure 11 This is a conventional ¹H MRS spectrum of Example 2 of the present invention; Figure 12 The J-edited¹H MRS spectrum of Embodiment 2 of the present invention; Figure 13 This is the neurotransmitter change curve of Embodiment 2 of the present invention; Figure 14 This refers to the task-state activation differences and resting-state brain functional connectivity changes in Embodiment 2 of the present invention; wherein, Figure 14 (a) shows that memory retrieval 30 minutes after overnight consolidation significantly activated brain regions under the condition of old image compared to new image; Figure 14 (b) Memory retrieval 30 minutes after overnight consolidation significantly activated brain regions under similar conditions compared to new conditions; Figure 15 This is a diagram showing the changes in resting-state brain functional connectivity in Embodiment 2 of the present invention; Figure 16 The sagittal and coronal views of Embodiment 3 of the present invention and the selected locations of the monomers are shown; wherein, Figure 16 (a) is in the sagittal position; Figure 16 (b) is the coronal position; Figure 17 This is a conventional ¹H MRS spectrum of Example 3 of the present invention; Figure 18 The J-edited¹H MRS spectrum of Embodiment 3 of the present invention; Figure 19 This is the neurotransmitter change curve of Example 3 of the present invention; Figure 20This refers to the task-state activation differences and resting-state brain functional connectivity changes in Embodiment 3 of the present invention; wherein, Figure 20 (a) shows that memory retrieval 30 minutes after overnight consolidation significantly activated brain regions under the condition of old image compared to new image; Figure 20 (b) Memory retrieval 30 minutes after overnight consolidation significantly activated brain regions under similar conditions compared to new conditions; Figure 20 (c) Memory retrieval after 30 minutes of memory consolidation showed significantly increased activation of brain regions in the old image compared to similar conditions; Figure 21 This is a diagram showing the changes in resting-state brain functional connectivity in Embodiment 3 of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0023] The research cycle of this invention is set into five stages: memory preparation, memory baseline, memory monitoring, memory retrieval, and memory stabilization. Each stage is progressive, enabling non-invasive, precise, and rapid monitoring of neurotransmitter changes in the brain caused by memory. It should be noted that the resting-state blood oxygenation level-dependent imaging pulse sequence is a gradient echo-planar echo imaging (GRE-EPI) sequence with a repetition time TR = 750 ms and an echo time TE = 30 ms; the short echo time sequence in the magnetic resonance spectroscopy imaging pulse sequence is a STEAM sequence (TE = 30 ms) or a PRESS sequence (TE = 30 ms), and the J-edit sequence is a MEGA-PRESS sequence (TE = 68 ms). The above sequence parameters can be optimized and adjusted according to different magnetic field strengths (e.g., 3T or 7T), but the acquisition parameters at each time point must be kept consistent.

[0024] Based on this, the present invention provides a method for assessing memory consolidation status, specifically proposing a magnetic resonance metabolic imaging method for monitoring changes in neurotransmitters in the brain caused by memory. For example... Figure 1 As shown, it includes the following steps: S1. Preparation and Establishment of Memory Data The first step is the memory preparation phase, which involves completing a scale and becoming familiar with the rules of the memory encoding task, providing initial support for subsequent assessment of changes in neurotransmitter levels caused by the memory encoding task.

[0025] like Figure 2As shown in the diagram, T1, T2, T3, and T4 represent the memory baseline period, memory monitoring period, memory retrieval period, and memory stabilization period, respectively. The memory baseline period includes baseline state and memory encoding; the memory monitoring period is for early memory consolidation; the memory retrieval period includes early memory retrieval and overnight memory retrieval; and the memory stabilization period is the baseline state after the overnight period. Figure 3 As shown, the individual status table indicates that the individual has completed the basic questionnaire, the memory encoding task is to familiarize themselves with the rules of the memory encoding task to facilitate the subsequent completion of the memory encoding task; the memory baseline task state Bold is the memory encoding task; the memory retrieval task state Bold is the memory retrieval, i.e., the recognition task.

[0026] The design of memory encoding tasks covers multiple domains, including visual memory and declarative memory, to comprehensively assess an individual's memory abilities. Task types should be personalized based on participants' cognitive abilities and task difficulty to ensure the challenge matches individual capabilities. During task-state Bold acquisition, task presentation and scanning are synchronized, and stimulus presentation and behavioral response times are accurately recorded to ensure the temporal correspondence between task events and brain activity signals. This multi-dimensional task design provides ample task support for assessing changes in brain functional connectivity and neurotransmitter fluctuations induced by memory encoding tasks, further revealing the neurochemical mechanisms and dynamic changes in brain functional networks during the memory process.

[0027] The baseline memory period then begins, including routine structural imaging, resting-state Bold (resting-state oxygenation level-dependent imaging), and MRS (magnetic resonance spectroscopy). Routine structural imaging typically employs high-resolution three-dimensional T1-weighted imaging with isotropic intra- and inter-slice resolution of ≤1 mm. Resting-state Bold scanning requires B1 mapping prior to the procedure to correct for Bold signal variations caused by B1 field inhomogeneities. Resting-state Bold scanning uses an EPI fast imaging sequence, requiring intra- and inter-slice resolution of ≤2.5 mm and at least 240 data acquisition periods. After the resting-state scan, an MRS scan is performed, which typically consists of two parts: The first part is a short-echo time-series MRS sequence, such as the STEAM or PRESS sequence, with a TE time of less than or equal to 30 ms and a region of interest volume greater than 8 cubic centimeters, used to measure the signals of metabolites such as glutamate (Glu), creatine, phosphocreatine, N-acetylaspartate, and total choline in the brain; the second part is a J-edit sequence, such as the MEGA-PRESS or MEGA-SLASER sequence, with a TE time of approximately 68 ms, and the radio frequency center of the edit pulse located around 1.9 ppm. The selected voxel position, size, and shimming parameters are the same as those of the short-echo time-series MRS sequence. The short-echo time-series MRS and the J-edit sequence together constitute an MRS module.

[0028] In this invention, resting-state oxygenation level-dependent imaging (OSA) data and magnetic resonance spectroscopy (MRS) imaging data are collected at multiple preset monitoring time points after the completion of the memory encoding task. During the experiment, OSA pulse sequences and MRS pulse sequences are applied alternately to preset brain regions of interest of the target subject. Specifically, after the completion of the memory encoding task, at least one phase of MRS data is collected first, followed by OSA data, and finally multiple phases of MRS data are collected. The OSA pulse sequence is an EPI sequence, and the MRS pulse sequence includes a short echo time series and a J-edit sequence.

[0029] S2, Data Acquisition During the Memory Monitoring Period After the memory encoding task is completed, the memory monitoring period begins. This period includes resting-state consolidation and multiple MRS imaging modules, and is the core acquisition stage of this invention. It should be noted that the brain imaging information acquisition method for memory consolidation provided by this invention processes already acquired magnetic resonance imaging data and does not include the step of performing a magnetic resonance scan on the target object. The change trajectory (co-change trajectory) generated by this invention is intermediate reference information used to assist researchers or clinicians in making comprehensive judgments; it does not itself directly lead to a disease diagnosis.

[0030] After the memory encoding task is completed, an MRS module scan is performed first to monitor immediate changes in neurotransmitters in the region of interest (ROI) at the end of the task. This is followed by a resting-state Bold scan using the same parameters as the baseline resting-state Bold scan to assess changes in functional connectivity across different brain regions after the task. Next, 3-6 MRS module scans are performed. The location, size, and sequence parameters of the RIO in each MRS module should be consistent to ensure data comparability and accuracy. The MRS sequence parameters used during the memory monitoring period are the same as those used during the memory baseline period. The total scanning time for the entire memory monitoring period should generally not exceed 40 minutes.

[0031] The magnetic resonance imaging (MRS) pulse sequence includes short echo time-series and J-edit sequences. Glutamate concentration is quantified using the short echo time-series, and γ-aminobutyric acid (GABA) concentration is quantified using the J-edit sequence. The pre-defined brain region of interest includes the medial prefrontal cortex. Bold scanning ensures that the acquired signal is free of significant artifacts, and the participant's head movement is controlled within permissible limits. Typically, the patient's head displacement during the scan is required to be no more than 2 mm, and the rotation angle no more than 2 degrees. The MRS scan ensures that the half-width at half-maximum (FWHM) of the water peak is less than or equal to 18 Hz, and the signal-to-noise ratio (SNR) of the MRS spectrum is greater than or equal to 20.

[0032] In this embodiment, multiple preset monitoring time points include 5 minutes, 20 minutes, 25 minutes, 30 minutes, and 24 hours after the completion of the memory encoding task; data is collected in the following sequence: 5 minutes after the completion of the memory encoding task, the first MRS scan is performed to acquire magnetic resonance spectroscopy imaging data; then a resting-state Bold scan is performed to acquire resting-state blood oxygenation level-dependent imaging data; then the second, third, and fourth MRS scans are performed at 20 minutes, 25 minutes, and 30 minutes after the completion of the memory encoding task, respectively; and the fifth MRS scan is performed 24 hours later. Each MRS scan includes a short echo time series and a J-edit sequence. The short echo time series is used to quantify glutamate concentration, the J-edit sequence is used to quantify γ-aminobutyric acid concentration, and the resting-state Bold scan uses an EPI sequence to acquire blood oxygenation level-dependent imaging data to assess brain functional connectivity.

[0033] S3, Cross-Night Memory Verification and Longitudinal Tracking Multiple preset monitoring time points include 5 minutes, 20 minutes, 25 minutes, 30 minutes, and 24 hours after the completion of the memory encoding task. The memory retrieval period includes two task-oriented memory retrieval Bold imaging modules spaced 24 hours apart. Task-oriented Bold scans acquire the activation status of different brain regions during the task by stimulating specific memory encoding tasks. During task-oriented Bold acquisition, task presentation and scanning timing are synchronized, and stimulus presentation and behavioral response times are accurately recorded to ensure the temporal correspondence between task events and brain activity signals.

[0034] The memory stabilization period includes a resting-state Bold and MRS scan to assess changes in brain functional connectivity and neurotransmitters after the completion of the memory encoding task. Typically, the memory monitoring period is more than 24 hours after the memory stabilization period, and the scan parameters are the same as those at the memory baseline.

[0035] S4. Data Processing and Generation of Memory Consolidation Status Information Both task-oriented and resting-state Bold data underwent preprocessing including time correction, head motion correction, registration, standardization, and smoothing to reduce interference from head movement and other noise factors. Head motion correction employed a rigid registration method to spatially align images at each time point, minimizing motion artifacts. Resting-state data underwent further filtering based on the above preprocessing to reduce low-frequency drift and physiological noise interference; task-oriented data was processed according to the task design and statistical analysis requirements.

[0036] Frequency domain transformation was performed on resting-state oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. Frequency domain transformation was also performed on magnetic resonance spectroscopy (MRS) imaging data to obtain MRS data. The frequency domain transformation employed the Fast Fourier Transform (FFT) algorithm, sequentially performing row and column Fourier transforms on the original k-space data at each time point, and applying Hanning or Gaussian windows for filtering to suppress truncation artifacts and noise. The reconstructed functional image sequence underwent phase and distortion correction to obtain multi-frame three-dimensional functional images with a temporal resolution of TR (repetition time). For the MRS data, the frequency domain transformation employed complex Fourier transform, and an exponential window function (linewidth broadening factor of 2–5 Hz) was applied for spectral smoothing to improve the signal-to-noise ratio. Zero-order and first-order phase corrections were then performed to obtain the real-part spectrum for subsequent quantitative analysis.

[0037] Functional connectivity strength values ​​between a preset brain region of interest (BRI) and the hippocampus were extracted from functional imaging sequences. The calculation of functional connectivity strength values ​​included: extracting the Bold time signal sequences of the preset BRI and the hippocampus; calculating the Pearson correlation coefficient between the two sequences; and using the correlation coefficient as the functional connectivity strength value. Glutamate and γ-aminobutyric acid (GABA) concentrations were extracted from the magnetic resonance spectroscopy data using the LCModel fitting algorithm; based on the glutamate and GABA concentrations, the excitability-inhibitory ratio of the target object was determined. The LCModel fitting used a base set provided by the manufacturer (including metabolite models such as Glu, GABA, tCr, tCho, and NAA), with a fitting range of 0.5–4.0 ppm. During fitting, residual water signals were subtracted, and baseline correction was automatically performed. The fitting uncertainty (%SD) for each metabolite concentration was required to be less than 20%, the water peak half-width (FWHM) ≤ 18 Hz, and the signal-to-noise ratio (SNR) ≥ 20. Simultaneously, a %SD ≤ 10% for tCr fitting was considered a quality control pass standard. If the %SD of a certain metabolite exceeds the threshold, the concentration data at that time point is marked as unreliable and removed to ensure the accuracy of subsequent trajectory fitting.

[0038] The MRS data post-processing process consists of two parts: MRS data preprocessing and data fitting. MRS data preprocessing includes steps such as phase correction, windowing, and zero filling to ensure the fitting accuracy of metabolite signals. MRS data fitting uses the corresponding basic set for fitting and uses non-aqueous suppression data to perform quantitative analysis of metabolite signals and assess the concentration changes of major neurotransmitters in the brain (such as Glu, GABA, etc.).

[0039] The functional connectivity strength value and the excitability-to-inhibition ratio at multiple preset monitoring time points are curve-fitted along a time axis to generate a coordinated change trajectory. The slope characteristics of the coordinated change trajectory are determined as follows: For any two adjacent preset monitoring time points t1 and t2 (t2>t1), the change in functional connectivity strength value or E / I ratio ΔV = V(t2) - V(t1) within this time interval is calculated, and the time interval Δt = t2 - t1. Then the slope k = ΔV / Δt. If k>+0.01 (or a specifically set positive threshold), it is defined as a positive slope, indicating that the index shows an upward trend within this time period, corresponding to the early enhancement stage of memory consolidation; if k<-0.01 (or a negative threshold), it is defined as a negative slope, indicating a downward trend, corresponding to the late decay or reorganization stage of memory consolidation; if |k|≤ 0.01, it is defined as a zero slope, indicating a stable trend, corresponding to the stable stage of memory consolidation. In practical applications, a global trend line (such as linear regression or locally weighted regression) can be fitted based on data from all time points to calculate the overall trend slope and divide the entire memory consolidation process into an early rising phase, a late falling phase, or a plateau phase. For example, if the functional connectivity strength value continuously increases (positive slope) from 5 to 30 minutes after memory encoding, and then decreases (negative slope) after 24 hours, it suggests a dynamic characteristic of initial enhancement followed by stabilization during memory consolidation. If the excitability-to-inhibition ratio (E / I ratio) maintains a positive slope across multiple intervals, it suggests a gradual increase in excitability dominance. The evaluation results are presented in a structured report with slope graphs and textual conclusions for clinical or research reference.

[0040] Based on the slope characteristics of the co-variation trajectory, the assessment results of the target object's memory consolidation status are output. The slope characteristics include positive slope, negative slope, and zero slope, corresponding to the early, late, and stable stages of memory consolidation, respectively. The co-variation trajectory and related data generated by this invention can be provided as intermediate reference information to clinicians or researchers for comprehensive judgment in conjunction with other clinical indicators; it does not directly provide a diagnosis of the disease itself.

[0041] The structured report aims to comprehensively analyze the impact of memory encoding tasks on brain functional connectivity and neurotransmitter changes. The report mainly includes four parts: ① Basic information: recording the participant's name, gender, scan time, and scan sequence; ② Data quality control: providing signal-to-noise ratio (SNR), full width at half maximum (FWHM), and head movement (HM); ③ Spectral results analysis: presenting conventional¹H MRS spectra, J-edited¹H MRS spectra, Glu concentration change curves, GABA concentration change curves, and E / I balance change curves (the ratio of excitability to inhibition) in image form; ④ Brain function and behavioral results analysis: including image-based presentation of brain functional changes and brain region connectivity changes after overnight memory acquisition, as well as resting-state functional connectivity changes and memory behavior results analysis. Specifically, this invention involves performing multiple magnetic resonance spectroscopy (MRS) and blood oxygen level-dependent (Bold) imaging acquisitions on the medial prefrontal cortex (mPFC) of subjects during a memory consolidation period (including the baseline period, multiple time points in the early stage of memory consolidation, and the overnight stabilization period). This successfully captured the dynamic concentration changes of glutamate (Glu), γ-aminobutyric acid (GABA), and excitation / inhibition (E / I) balance induced by memory tasks. Combined with resting-state and task-state functional connectivity analysis and behavioral accuracy (ACC), a traceable temporal correlation was found between neurotransmitter fluctuations and the strength of the medial prefrontal cortex-hippocampus (mPFC-HPC) functional connectivity and memory retrieval effectiveness. This verifies that the magnetic resonance metabolic imaging method provided by this invention can non-invasively, accurately, and sensitively monitor memory-related neurochemical and network plasticity changes in the brain in a longitudinal manner.

[0042] Example 1: Example 1 shows the results of a single measurement during the memory baseline period, four measurements during the memory monitoring period, and one measurement during the memory stabilization period of a healthy 21-year-old female college student.

[0043] This is a structured report from Embodiment 1 of the present invention. The collected data passed quality control, and the memory behavior result analysis was ACC. 30min =75%; ACC 24h=75%. Here, ACC represents accuracy; 30 min refers to 30 minutes after memory consolidation; 24 h refers to 24 hours after memory consolidation. Specifically: The subject was a healthy female who completed J-edited sequences and routine MRS and other multi-sequence acquisitions in scanning protocols of approximately 60 minutes (day 1) and 120 minutes (day 2); all quality control indicators for MRS and Bold data (FWHM≤18Hz, SNR≥20, head movement <2 mm / 2°) met the preset thresholds, ensuring the reliability of subsequent analysis; spectral results and metabolite dynamic curves revealed temporal fluctuations in Glu, GABA concentrations and E / I balance during memory consolidation, while resting-state functional connectivity showed a trend of first increasing and then decreasing, and task-oriented brain function changes and behavioral performance (ACC) were correlated. 30min =75%, ACC 24h The combined results (75%) validated that this imaging method can effectively monitor dynamic changes in memory-related neurochemical and functional networks.

[0044] like Figures 4-6 The image shows the localization and spectral acquisition results of monomers in the mPFC brain region according to Example 1 of the present invention. Among them, Figure 4 (a) represents the position of the haploid selected in the sagittal position and Figure 4 (b) The location of the selected voxel in the coronal view. Both views point to the same voxel and are used to show the spatial localization of the MRS-acquired voxel in the mPFC brain region. Figure 5 The image shows a standard ¹H MRS spectrum, with the measured spectrum (black) and the fitted curve (red). The more the two curves overlap, the higher the signal-to-noise ratio, the better the fitting accuracy, and the more reliable the quantified metabolite concentration. Figure 6 The J¹H MRS spectrum is edited, showing the measured spectrum (black) and the fitted curve (red). The degree of overlap between the two curves reflects the fitting quality and signal-to-noise ratio, and is used to quantify the concentration of γ-aminobutyric acid (GABA).

[0045] like Figure 7 The diagram shows the neurotransmitter change curves of Embodiment 1 of the present invention. 1-6 represent the times of six sequential MRS scans in the flowchart. The Glu concentration change curve is represented by a gray line, the GABA concentration change curve by a red line, and the excitability-to-inhibitory ratio change curve by a blue line. Glu concentration and E / I balance show a trend of first decreasing and then stabilizing during the memory consolidation process, while GABA concentration fluctuates.

[0046] like Figures 8-9 As shown, this illustrates the differences in task-state activation and resting-state brain functional connectivity in Embodiment 1 of the present invention. Figure 8 (a) shows that overnight memory retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation, under the condition of old image versus new image. Figure 8(b) Overnight memory retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation under similar conditions compared to new images; Figure 9 The changes in resting-state brain functional connectivity show a trend of first increasing and then decreasing.

[0047] All three examinations were performed on a Siemens 3.0T MRI system, using the MEGA-PRESS sequence of this invention with an echo time of 68ms, a repetition time of 1800ms, a sampling bandwidth of 2000Hz, 1024 sampling points, and a signal averaging frequency of 64 times. The resting-state scan parameters were: repetition time 750ms, echo time 30ms, matrix 90×90, field of view 216×216mm², flip angle 60°, number of slices 60, slice thickness 2.4mm, and voxel size 2.4×2.4×2.4mm³. The same parameters were used for both resting-state and task-state functional images.

[0048] The mPFC brain region was determined in the sagittal plane, and the monoclonal antibody was placed within the mPFC brain region, avoiding areas such as the sinus nucleus as much as possible. Frequency domain transformation was performed on the acquired resting-state blood oxygenation level-dependent imaging data to reconstruct multi-time-point functional image sequences. Bold time signal sequences of the medial prefrontal cortex and hippocampus were extracted, and Pearson correlation coefficients were calculated to obtain functional connectivity strength values. Frequency domain transformation was performed on the acquired magnetic resonance spectroscopy imaging data to obtain magnetic resonance spectroscopy data. Using the LCModel fitting algorithm and the corresponding baseline set, metabolite signals were quantitatively analyzed to extract glutamate and γ-aminobutyric acid (GABA) concentrations. The ratio of glutamate to GABA concentrations was calculated to obtain the E / I value. The functional connectivity strength values ​​and E / I values ​​at each preset monitoring time point were curve-fitted along the time axis to generate a co-variance trajectory. The memory consolidation status was determined based on the trajectory slope.

[0049] After preprocessing, the raw spectral data were fitted to obtain the concentration of each metabolite and the fitting uncertainty. At the same time, the water peak half width at half maximum, signal-to-noise ratio (SNR), and tCr fitting %SD were evaluated for quality control. All three scans met the preset quality control thresholds, and the quality control was passed.

[0050] The fitting results of Glu, GABA and E / I from six scans are as follows: Figure 7As shown. At baseline, the Glu concentration was 6.833 mM, the GABA concentration was 0.554 mM, and the E / I value was 12.334. After 5 minutes of memory consolidation, under the same voxel and acquisition parameters, the Glu concentration was 4.700 mM, the GABA concentration was 0.577 mM, and the E / I value was 8.146, indicating an early decreasing trend in Glu levels in the mPFC brain region. After 20 minutes of memory consolidation, the Glu concentration was 6.991 mM, the GABA concentration was 0.552 mM, and the E / I value was 12.665, indicating a rebound trend in Glu levels. After 25 minutes of memory consolidation, the Glu concentration was 6.267 mM. The GABA concentration was 0.511 mM and the E / I value was 12.264, indicating that the Glu level tended to stabilize with a slight downward trend. After 30 minutes of memory consolidation, the Glu concentration was 6.623 mM, the GABA concentration was 0.590 mM, and the E / I value was 11.225, indicating that the Glu level continued to decline, and the GABA level was relatively stable in the early memory consolidation stage. After 24 hours of memory consolidation, the Glu concentration was 6.950 mM, the GABA concentration was 0.472 mM, and the E / I value was 14.725, indicating that the Glu level in the mPFC brain region showed a cross-night recovery or even an increase, while the GABA level showed a cross-night decrease.

[0051] Brain function results of two memory retrievals as follows Figure 8 (a) and Figure 8 As shown in (b), in cross-night memory retrieval compared to memory retrieval 30 minutes after consolidation, under the condition of old image versus new image, larger salient clusters were mainly distributed in the right lingual gyrus, calcarine gyrus, and cuneus; the left inferior temporal gyrus, middle occipital gyrus, and fusiform gyrus; the left supplementary motor area and middle cingulate gyrus; the left inferior frontal gyrus and left inferior parietal lobule and precuneus; and bilateral cerebellum. This suggests that, compared to retrieval after 30 minutes of memory consolidation, cross-night old image retrieval involves more visual representation reactivation, recognition and retrieval of stored items, and brain networks related to executive control and response regulation. Under the condition of similarity versus new image, the clusters were mainly distributed in the left inferior frontal gyrus, middle frontal gyrus, and left precuneus, extending to the angular gyrus, inferior parietal lobule, and superior parietal lobule. This suggests that, compared to new image, similar image retrieval after overnight retrieval may involve more prefrontal-parietal network involvement, reflecting cognitive processing related to representation matching, retrieval monitoring, and familiarity judgment. Meanwhile, the overall accuracy rate of memory retrieval after 30 minutes of memory consolidation was 75%, and the overall accuracy rate of overnight memory retrieval was 63.33%, suggesting that memory status declined after overnight retrieval.

[0052] The results of three resting-state functional connections are as follows Figure 9As shown, at baseline, the FC connectivity strength (functional connectivity strength value) between the mPFC and HPC brain regions was 0.816; during the memory monitoring period, the FC connectivity strength was 1.064, indicating an enhanced connectivity strength between the mPFC and HPC, suggesting the occurrence of memory storage; during the memory stabilization period, the FC connectivity strength was 0.876, indicating that the connectivity strength recovered across nights and was higher than at baseline.

[0053] Based on the quantitative results of six measurements and stable quality control indicators, it is evident that, under controllable systematic errors, Glu concentration initially decreases and then stabilizes during memory consolidation, while GABA concentration fluctuates. This reflects a decrease in excitability in the mPFC brain region during the initial stage of memory consolidation, corresponding to an initial increase and subsequent recovery of FC connectivity between the mPFC and HPC brain regions. This also corresponds to higher memory retrieval efficiency 30 minutes after consolidation and better overnight memory retrieval performance. This embodiment demonstrates that the magnetic resonance metabolic imaging method for detecting neurotransmitter changes induced by memory, as described in this invention, can provide sensitive and reproducible metabolic endpoints in longitudinal monitoring and can serve as biomarkers for predicting memory performance.

[0054] Example 2: Example 2 shows the results of a healthy 23-year-old female college student during the memory baseline period, four measurements during the memory monitoring period, and one measurement during the memory stabilization period.

[0055] The data collection in Example 2 passed quality control, and the memory behavior results analysis was ACC. 30min =65%; ACC 24h =50%. Here, ACC represents accuracy; 30min refers to 30 minutes after memory consolidation; 24h refers to 24 hours after memory consolidation. Specifically: Subject sub2 was a healthy woman who completed J-edited sequences and routine MRS and other multi-sequence acquisition within two days. All quality control indicators for MRS and Bold data (FWHM≤18 Hz, SNR≥20, head movement <2 mm / 2°) met the requirements. Spectroscopic results and metabolite dynamic curves reflected the temporal changes in Glu, GABA concentrations and E / I balance during memory consolidation. Resting-state functional connectivity showed a trend of first increasing and then decreasing. Task-based brain function changes showed a decrease in cross-night memory retrieval performance. Behavioral results (ACC...) 30min =65%, ACC 24h The 50% (=50%) further validated that this imaging method can effectively monitor the dynamics of memory-related neurotransmitters and the plasticity changes of functional networks.

[0056] like Figures 10-12 The image shows the results of localization and spectral acquisition of monomorphic elements in the mPFC brain region in Example 2 of this invention. Figure 10 (a) represents the position of the haploid selected in the sagittal position and Figure 10 (b) The location of the monomorphic voxel selected in the coronal view, the sagittal view and the coronal view are two different perspectives of the same monomorphic voxel, respectively showing the spatial location of the monomorphic voxel in the mPFC brain region from the side and front, which is used to show the location of the MRS-collected voxel in the mPFC brain region and the interference areas avoided. Figure 11 The image shows a standard ¹H MRS spectrum, with the measured spectrum (black) and the fitted curve (red). This sequence is primarily used for quantifying glutamate (Glu) concentration. Figure 12 Edit the ¹H MRS spectrum for J, the measured spectrum (black) and the fitted curve (red). This sequence is mainly used to quantify the concentration of γ-aminobutyric acid (GABA). Figure 11 and Figure 12 The higher the degree of overlap between the measured spectrum and the fitted curve, the better the fitting quality and signal-to-noise ratio, and the more reliable the quantitative results.

[0057] like Figure 13 The diagram shows the neurotransmitter change curves in Embodiment 2 of the present invention. 1-6 represent the times of the six sequential MRS scans in the flowchart. The Glu concentration change curve is represented by a gray line, the GABA concentration change curve by a red line, and the E / I balance change curve by a blue line. Glu concentration and E / I balance show a trend of first increasing, then decreasing, and finally stabilizing as memory consolidation progresses. GABA concentration shows a trend of first decreasing, then increasing, and then decreasing again in the early consolidation stage.

[0058] like Figures 14-15 As shown, this illustrates the differences in task-state activation and resting-state brain functional connectivity in Embodiment 2 of the present invention. Figure 14 (a) shows that memory retrieval after 30 minutes of memory consolidation and memory retrieval after 30 minutes of memory consolidation, compared to memory retrieval after 30 minutes of memory consolidation, significantly activated brain regions under the condition of old image compared to new image; Figure 14 (b) Overnight memory retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation under similar conditions compared to new images; Figure 15 The changes in resting-state brain functional connectivity show a trend of first increasing and then decreasing.

[0059] All three examinations were performed on a Siemens 3.0T MRI system, using the MEGA-PRESS sequence of this invention with an echo time of 68ms, a repetition time of 1800ms, a sampling bandwidth of 2000Hz, 1024 sampling points, and a signal averaging frequency of 64 times. The resting-state scan parameters were: repetition time 750ms, echo time 30ms, matrix 90×90, field of view 216×216mm², flip angle 60°, number of slices 60, slice thickness 2.4mm, and voxel size 2.4×2.4×2.4mm³. The same parameters were used for both resting-state and task-state functional images.

[0060] The mPFC brain region was determined in the sagittal plane, and the monoclonal antibody was placed within the mPFC brain region, avoiding areas such as the sinus nucleus as much as possible. Frequency domain transformation was performed on the acquired resting-state blood oxygenation level-dependent imaging data to reconstruct multi-time-point functional image sequences. Bold time signal sequences of the medial prefrontal cortex and hippocampus were extracted, and Pearson correlation coefficients were calculated to obtain functional connectivity strength values. Frequency domain transformation was performed on the acquired magnetic resonance spectroscopy imaging data to obtain magnetic resonance spectroscopy data. Using the LCModel fitting algorithm and the corresponding baseline set, metabolite signals were quantitatively analyzed to extract glutamate and γ-aminobutyric acid (GABA) concentrations. The ratio of glutamate to GABA concentrations was calculated to obtain the E / I value. The functional connectivity strength values ​​and E / I values ​​at each preset monitoring time point were curve-fitted along the time axis to generate a co-variance trajectory. The memory consolidation status was determined based on the trajectory slope.

[0061] After preprocessing, the raw spectral data were fitted to obtain the concentration of each metabolite and the fitting uncertainty. At the same time, the water peak half width at half maximum, signal-to-noise ratio (SNR), and tCr fitting %SD were evaluated for quality control. All three scans met the preset quality control thresholds, and the quality control was passed.

[0062] The fitting results of Glu, GABA and E / I from six scans are as follows: Figure 13 As shown. At baseline, the Glu concentration was 6.179 mM, the GABA concentration was 0.547 mM, and the E / I value was 11.296. After 5 minutes of memory consolidation, under the condition of consistent voxel and acquisition parameters, the Glu concentration was 6.306 mM, the GABA concentration was 0.472 mM, and the E / I value was 13.360, indicating an upward trend in Glu levels and a downward trend in GABA levels in the mPFC brain region. After 20 minutes of memory consolidation, the Glu concentration was 5.843 mM, the GABA concentration was 0.515 mM, and the E / I value was 11.346, indicating a downward trend in Glu levels and a rebound trend in GABA levels. After 25 minutes of memory consolidation... At 12:00 AM, the Glu concentration was 6.574 mM, the GABA concentration was 0.556 mM, and the E / I value was 11.824, indicating a rising trend in Glu levels and a continued rise in GABA levels. After 30 minutes of memory consolidation, the Glu concentration was 6.552 mM, the GABA concentration was 0.488 mM, and the E / I value was 13.426, indicating that Glu levels were stabilizing while GABA levels were decreasing. After 24 hours of memory consolidation, the Glu concentration was 6.179 mM, the GABA concentration was 0.577 mM, and the E / I value was 10.709, indicating that Glu levels in the mPFC brain region recovered overnight, and GABA levels also recovered overnight or even increased. Meanwhile, the overall accuracy rate of memory retrieval after 30 minutes of consolidation was 65%, and the overall accuracy rate of overnight memory retrieval was 50%, suggesting a decline in memory performance after overnight consolidation.

[0063] Brain function results of two memory retrievals as follows Figure 14 (a) and Figure 14 As shown in (b), in memory retrieval 30 minutes after consolidation compared to overnight memory retrieval, under the condition of old image vs. new image, salient clusters were mainly distributed in the bilateral occipital cortex. Specifically, on the right side, they were mainly located in the cuneus, calcarine sulcus, and lingual gyrus, while on the left side, they were mainly located in the superior occipital gyrus, middle occipital gyrus, and cuneus, indicating overall involvement of visual cortical areas. In overnight memory retrieval compared to memory retrieval 30 minutes after consolidation, under the condition of old image vs. new image, salient clusters were mainly distributed in the right parietal network, specifically including the right superior parietal lobule, precuneus, and right middle frontal gyrus. In overnight memory retrieval compared to memory retrieval 30 minutes after consolidation, under the condition of similarity vs. new image, this result was mainly distributed in the left postcentral gyrus, extending to the left inferior parietal lobule, primarily involving somatosensory cortical areas.

[0064] The results of three resting-state functional connections are as follows Figure 15 As shown, at baseline, the FC connection strength between the mPFC and HPC brain regions was 0.641; during the memory monitoring period, the FC connection strength was 0.894, indicating an increase in the connection strength between the mPFC and HPC, suggesting the occurrence of memory storage; during the memory stabilization period, the FC connection strength was 0.619, indicating that the connection strength recovered across nights.

[0065] Based on the quantitative results of six measurements and stable quality control indicators, it can be seen that, under the premise of controllable systematic error, Glu concentration initially increases, then decreases, and finally stabilizes as memory consolidation progresses. GABA concentration initially decreases, then increases, and then decreases again in the early consolidation stage, reflecting a decrease followed by an increase in the degree of inhibition in the mPFC brain region during the initial stage of memory consolidation, while the degree of excitation also shows a decreasing trend in the middle. This corresponds to the trend of the FC connection between the mPFC and HPC brain regions initially increasing and then recovering overnight, which also corresponds to a higher memory retrieval effect 30 minutes after memory consolidation and overnight memory retrieval effect. This embodiment shows that the magnetic resonance metabolic imaging method for memory-induced neurotransmitter changes of the present invention can provide sensitive and reproducible metabolic endpoints in longitudinal monitoring and can serve as a biomarker for predicting memory performance.

[0066] Example 3: Example 3 shows the results of a healthy 18-year-old male college student during the memory baseline period, four measurements during the memory monitoring period, and one measurement during the memory stabilization period.

[0067] This is a structured report from Embodiment 3 of the present invention. The collected data passed quality control, and the memory behavior result analysis was ACC. 30min =77%; ACC 24h=78%. Where ACC represents the accuracy rate; 30min refers to the time after 30 minutes of memory consolidation; 24h refers to the time after 24 hours of memory consolidation.

[0068] Subject sub3 in Example 3 was a healthy male who completed J-edited sequences and routine MRS and other multi-sequence acquisition within two days. All quality control indicators for MRS and Bold data (FWHM ≤ 18 Hz, SNR ≥ 20, head movement < 2 mm / 2°) met the preset requirements. Spectroscopic results and metabolite dynamic curves showed temporal fluctuations in Glu and GABA concentrations and E / I balance during memory consolidation. Resting-state functional connectivity showed a trend of first increasing and then decreasing. Task-oriented brain function changes and behavioral performance (ACC) were also observed. 30min =77%, ACC 24h The combined results (78%) validated that this imaging method can effectively monitor memory-related neurochemical dynamics and changes in functional network plasticity.

[0069] like Figures 16-18 The image shows the localization and spectral acquisition results of the mPFC brain region monomers in Example 3 of this invention. Figure 16 (a) represents the position of the haploid selected in the sagittal position and Figure 16 (b) The location of the monomorphic voxel selected in the coronal view, the sagittal view and the coronal view are two different perspectives of the same monomorphic voxel, respectively showing the spatial location of the monomorphic voxel in the mPFC brain region from the side and front, which is used to show the location of the MRS-collected voxel in the mPFC brain region and the interference areas avoided. Figure 17 The image shows a standard ¹H MRS spectrum, with the measured spectrum (black) and the fitted curve (red). This sequence is primarily used for quantifying glutamate (Glu) concentration. Figure 18 Edit the ¹H MRS spectrum for J, the measured spectrum (black) and the fitted curve (red). This sequence is mainly used to quantify the concentration of γ-aminobutyric acid (GABA). Figure 17 and Figure 18 The higher the degree of overlap between the measured spectrum and the fitted curve, the better the fitting quality and signal-to-noise ratio, and the more reliable the quantitative results.

[0070] like Figure 19 The diagram shows the neurotransmitter change curves in Embodiment 3 of the present invention. 1-6 represent the times of the six sequential MRS scans in the flowchart. The Glu concentration change curve is represented by a gray line, the GABA concentration change curve by a red line, and the E / I balance change curve by a blue line. Glu concentration and E / I balance generally show a decreasing trend during the memory consolidation process, while GABA concentration shows some fluctuations, ultimately trending downwards.

[0071] like Figures 20-21 As shown, this illustrates the differences in task-state activation and resting-state brain functional connectivity in Embodiment 3 of the present invention. Figure 20(a) shows that memory retrieval after 30 minutes of memory consolidation and memory retrieval after 30 minutes of memory consolidation, compared to memory retrieval after 30 minutes of memory consolidation, significantly activated brain regions under the condition of old image compared to new image; Figure 20 (b) Overnight memory retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation under similar conditions compared to new images; Figure 20 (c) Overnight memory retrieval significantly activated brain regions compared to memory retrieval 30 minutes after memory consolidation, with old images compared to similar conditions; Figure 21 The changes in resting-state brain functional connectivity show a continuous upward trend.

[0072] All three examinations were performed on a Siemens 3.0T MRI system, using the MEGA-PRESS sequence of this invention with an echo time of 68ms, a repetition time of 1800ms, a sampling bandwidth of 2000Hz, 1024 sampling points, and a signal averaging frequency of 64 times. The resting-state scan parameters were: repetition time 750ms, echo time 30ms, matrix 90×90, field of view 216×216mm², flip angle 60°, number of slices 60, slice thickness 2.4mm, and voxel size 2.4×2.4×2.4mm³. The same parameters were used for both resting-state and task-state functional images.

[0073] The mPFC brain region was determined in the sagittal plane, and the monoclonal antibody was placed within the mPFC brain region, avoiding areas such as the sinus nucleus as much as possible. Frequency domain transformation was performed on the acquired resting-state blood oxygenation level-dependent imaging data to reconstruct multi-time-point functional image sequences. Bold time signal sequences of the medial prefrontal cortex and hippocampus were extracted, and Pearson correlation coefficients were calculated to obtain functional connectivity strength values. Frequency domain transformation was performed on the acquired magnetic resonance spectroscopy imaging data to obtain magnetic resonance spectroscopy data. Using the LCModel fitting algorithm and the corresponding baseline set, metabolite signals were quantitatively analyzed to extract glutamate and γ-aminobutyric acid (GABA) concentrations. The ratio of glutamate to GABA concentrations was calculated to obtain the E / I value. The functional connectivity strength values ​​and E / I values ​​at each preset monitoring time point were curve-fitted along the time axis to generate a co-variance trajectory. The memory consolidation status was determined based on the trajectory slope.

[0074] After preprocessing, the raw spectral data were fitted to obtain the concentration of each metabolite and the fitting uncertainty. At the same time, the water peak half width at half maximum, signal-to-noise ratio (SNR), and tCr fitting %SD were evaluated for quality control. All three scans met the preset quality control thresholds, and the quality control was passed.

[0075] The fitting results of Glu, GABA and E / I from six scans are as follows: Figure 19As shown. At baseline, Glu concentration was 6.860 mM, GABA concentration was 0.485 mM, and E / I value was 14.144. After 5 minutes of memory consolidation, under the same voxel and acquisition parameters, the Glu concentration was 6.045 mM, GABA concentration was 0.488 mM, and E / I value was 12.387, indicating a decreasing trend in Glu level and relatively stable GABA level in the mPFC brain region. After 20 minutes of memory consolidation, Glu concentration was 6.323 mM, GABA concentration was 0.417 mM, and E / I value was 15.163, indicating a rebound trend in Glu level and a decreasing trend in GABA level. After 25 minutes of memory consolidation, G... The Glu concentration was 6.159 mM, the GABA concentration was 0.549 mM, and the E / I value was 11.219, indicating a slight decreasing trend in Glu levels and an increasing trend in GABA levels. After 30 minutes of memory consolidation, the Glu concentration was 6.017 mM, the GABA concentration was 0.370 mM, and the E / I value was 16.262, indicating a continued decrease in Glu levels and a significant decrease in GABA levels. After 24 hours of memory consolidation, the Glu concentration was 6.234 mM, the GABA concentration was 0.499 mM, and the E / I value was 12.493, indicating a recovery in Glu levels across nights and a recovery or even an increase in GABA levels within the mPFC brain region. Meanwhile, the overall accuracy rate of memory retrieval after 30 minutes of consolidation was 76.667%, and the overall accuracy rate of overnight memory retrieval was 78.333%, suggesting an improvement in memory status after overnight consolidation.

[0076] Brain function results of two memory retrievals as follows Figure 20 and Figure 21As shown. For memory retrieval across nights compared to memory retrieval 30 minutes after consolidation, under the condition of comparing the old image to the new image, the larger clusters were located in the right angular gyrus, superior parietal lobule, inferior parietal lobule, and left superior parietal lobule and inferior parietal lobule. The frontal lobe distribution mainly involved the bilateral middle frontal gyrus, superior frontal gyrus, precentral gyrus, left supplementary motor area, left posterior cerebellum, and right temporo-occipital region. For memory retrieval across nights compared to memory retrieval 30 minutes after consolidation, under the condition of comparing the old image to the new image, the results showed that significant clusters were mainly concentrated in the bilateral posterior cerebellum, left superior temporal gyrus, right inferior frontal gyrus, left supplementary motor area, and bilateral paracentral lobules. For memory retrieval 30 minutes after consolidation compared to memory retrieval across nights, under the condition of comparing the old image to the new image, significant clusters were mainly located in the left angular gyrus-inferior parietal lobule region, partially involving the left precuneus, with small significant distributions also visible in the right middle frontal gyrus and precentral gyrus. Compared to memory retrieval 30 minutes after memory consolidation, in terms of overnight memory retrieval and under similar conditions, significant clusters were mainly concentrated in the bilateral parietal and frontal lobes. Larger clusters were located in the left superior parietal lobule, inferior parietal lobule, anterior and posterior central gyrus, and middle frontal gyrus, as well as the right angular gyrus, superior parietal lobule, inferior parietal lobule, and postcentral gyrus. At the same time, there were large-scale distributions in the right superior frontal gyrus, middle frontal gyrus, and anterior central gyrus. In addition, small to medium-sized significant clusters were visible in the right temporo-occipital region and the left temporo-occipital region.

[0077] The results of three resting-state functional connections are as follows Figure 20 As shown in (c), the FC connection strength between the mPFC and HPC brain regions was 0.454 at baseline; during the memory monitoring period, the FC connection strength was 0.579, indicating an increase in the connection strength between the mPFC and HPC, suggesting the occurrence of memory storage; during the memory stabilization period, the FC connection strength was 0.905, indicating a cross-night increase in the connection strength after the night.

[0078] Based on the quantitative results of six measurements and stable quality control indicators, it can be seen that, under the premise of controllable systematic error, Glu concentration generally shows a decreasing trend with the memory consolidation process, while GABA concentration shows more fluctuations, with a final decreasing trend. This reflects the opposite fluctuations in excitation and inhibition in the mPFC brain region during the early stage of memory consolidation. It also corresponds to the trend of FC connectivity between the mPFC and HPC brain regions first increasing and then increasing across nights, which also corresponds to higher memory retrieval after 30 minutes of consolidation and higher cross-night memory retrieval efficiency. This embodiment demonstrates that the magnetic resonance metabolic imaging method for memory-induced neurotransmitter changes of this invention can provide sensitive and reproducible metabolic endpoints in longitudinal monitoring and can serve as biomarkers for predicting memory performance.

[0079] Compared with the prior art, the present invention has the following advantages: (1) This invention provides a non-invasive, precise, and rapid magnetic resonance metabolic method for monitoring changes in neurotransmitters in the brain induced by memory encoding tasks. Compared with traditional methods for studying memory mechanisms (such as combining scales, resting-state Bold, and task-state Bold), this method combines task-state Bold with multi-phase MRS acquisition to achieve real-time monitoring of brain network states and neurotransmitter changes. This method can capture the dynamic changes in brain region activation and neurotransmitter fluctuations induced by memory encoding tasks, reveal the role of neurotransmitters in brain functional activities, and provide strong technical support for the early diagnosis of cognitive dysfunction and the intervention of related diseases.

[0080] (2) The MRS module used in this invention includes a short TE MRS sequence and a J-edit sequence. Compared with conventional MRS methods, it can detect brain metabolites in a short time and achieve accurate detection of low concentration neurotransmitters, which significantly improves the sensitivity and accuracy of neurotransmitter changes.

[0081] (3) This invention provides a complete method for assessing brain memory mechanisms, covering the memory preparation period, memory baseline period, memory monitoring period, memory retrieval period and memory stabilization period. Each stage is clearly defined and closely connected, providing strong support for comprehensively and accurately monitoring the impact of memory encoding tasks on changes in neurotransmitters in the brain.

[0082] (4) This invention will generate a structured report for each subject. The report includes not only the participant’s basic information and scale scores, but also shows the changes in functional connectivity patterns of different brain regions and the fluctuations in neurotransmitter concentrations before and after the task, providing detailed quantitative data for a deeper understanding of the neural mechanisms of memory.

[0083] Based on the same inventive concept, the present invention also provides a memory consolidation state assessment system, comprising: The data acquisition module is used to acquire resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data of the target object at multiple preset monitoring time points during the memory consolidation process after the completion of the memory encoding task; the resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data are acquired at different preset monitoring time points.

[0084] The computational module performs frequency domain transformation on resting-state blood oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. It extracts functional connectivity strength values ​​between a preset brain region of interest and the hippocampus from the functional image sequence; these values ​​characterize the brain functional network coordination state of the target subject during memory consolidation. The module also performs frequency domain transformation on magnetic resonance spectroscopy (MRS) imaging data to obtain MRS data. Using the LCModel fitting algorithm, it extracts glutamate and γ-aminobutyric acid (GABA) concentrations from the MRS data. Based on these concentrations, it determines the excitability-inhibition ratio of the target subject; this ratio characterizes the neurotransmitter metabolic balance state of the target subject in the preset brain region of interest during memory consolidation.

[0085] The trajectory generation module is used to perform curve fitting on the functional connectivity strength value and the ratio of excitability to inhibition at multiple preset monitoring time points along the time axis to generate a trajectory that characterizes the changes in the neurotransmitter metabolic balance and brain functional network coordination state of the target object during the memory consolidation process.

[0086] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the memory consolidation state assessment method provided above.

[0087] The present invention also provides a computer-readable storage medium storing a computer program that can be used to perform the memory consolidation state assessment method provided above.

[0088] Specific limitations regarding the computational system for the memory consolidation assessment method can be found in the limitations of the memory consolidation assessment method described above, and will not be repeated here. Each module in the aforementioned memory consolidation assessment system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0089] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, the above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for assessing memory consolidation status, characterized in that, Includes the following steps: The system acquires resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data at multiple preset monitoring time points during the memory consolidation process of the target object after the completion of the memory encoding task; the resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data are acquired at different preset monitoring time points. Frequency domain transformation was performed on resting-state blood oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. Functional connectivity strength values ​​between a preset brain region of interest and the hippocampus were extracted from the functional image sequence. These functional connectivity strength values ​​characterize the brain functional network coordination state of the target subject during the memory consolidation process. Frequency domain transformation was also performed on magnetic resonance spectroscopy (MRS) imaging data to obtain MRS data. Glutamate and γ-aminobutyric acid (GABA) concentrations were extracted from the MRS data using the LCModel fitting algorithm. Based on these glutamate and GABA concentrations, the excitability-to-inhibition ratio of the target subject was determined. This excitability-to-inhibition ratio characterizes the neurotransmitter metabolic balance state of the target subject in the preset brain region of interest during the memory consolidation process. The functional connectivity strength value and the ratio of excitability to inhibition at multiple preset monitoring time points are curve-fitted along the time axis to generate a trajectory that characterizes the changes in the neurotransmitter metabolic balance and brain functional network coordination state of the target object during the memory consolidation process.

2. The method for assessing memory consolidation status according to claim 1, characterized in that, The resting-state oxygenation level-dependent imaging data is acquired based on a resting-state oxygenation level-dependent imaging pulse sequence, and the magnetic resonance spectroscopy imaging data is acquired based on a magnetic resonance spectroscopy imaging pulse sequence.

3. The method for assessing memory consolidation status according to claim 1, characterized in that, The magnetic resonance spectroscopy imaging is a multi-phase magnetic resonance spectroscopy imaging, with 3 to 6 phases; the multiple preset monitoring time points include the baseline state before the memory coding task and time points at 5 minutes, 20 minutes, 25 minutes, 30 minutes and 24 hours after the completion of the memory coding task.

4. The method for assessing memory consolidation status according to claim 2, characterized in that, The magnetic resonance spectroscopy imaging pulse sequence includes a short echo time sequence and a J-edit sequence; the glutamate concentration is quantified by the short echo time sequence, and the γ-aminobutyric acid concentration is quantified by the J-edit sequence.

5. The method for assessing memory consolidation status according to claim 1, characterized in that, The preset brain region of interest includes the medial prefrontal cortex.

6. The method for assessing memory consolidation status according to claim 1, characterized in that, The slope characteristics of the change trajectory include positive slope, negative slope, and zero slope, which respectively indicate that at the corresponding preset monitoring time point, the functional connection strength value or the ratio of excitability to inhibition shows an upward trend, a downward trend, and a stable trend.

7. The method for assessing memory consolidation status according to claim 1, characterized in that, The calculation of the functional connectivity strength value includes: extracting the Bold time signal sequence of a preset brain region of interest and the Bold time signal sequence of the hippocampus brain region, calculating the Pearson correlation coefficient between the two sequences, and using the correlation coefficient as the functional connectivity strength value.

8. A memory consolidation state assessment system, characterized in that, include: The data acquisition module is used to acquire resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data of the target object at multiple preset monitoring time points during the memory consolidation process after the completion of the memory encoding task; the resting oxygen level-dependent imaging data and magnetic resonance spectroscopy imaging data are acquired at different preset monitoring time points; The computation module performs frequency domain transformation on resting-state blood oxygenation level-dependent imaging data to reconstruct a multi-time-point functional image sequence. It then extracts functional connectivity strength values ​​between a preset brain region of interest and the hippocampus from the functional image sequence. These functional connectivity strength values ​​characterize the brain functional network coordination state of the target subject during the memory consolidation process. The module also performs frequency domain transformation on magnetic resonance spectroscopy imaging data to obtain magnetic resonance spectroscopy data. Using the LCModel fitting algorithm, it extracts glutamate and γ-aminobutyric acid (GABA) concentrations from the magnetic resonance spectroscopy data. Based on these glutamate and GABA concentrations, it determines the excitability-inhibition ratio of the target subject. This excitability-inhibition ratio characterizes the neurotransmitter metabolic balance state of the target subject in the preset brain region of interest during the memory consolidation process. The trajectory generation module is used to perform curve fitting on the functional connectivity strength value and the ratio of excitability to inhibition at multiple preset monitoring time points along the time axis to generate a trajectory that characterizes the changes in the neurotransmitter metabolic balance and brain functional network coordination state of the target object during the memory consolidation process.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to perform the steps of the method according to any one of claims 1 to 7.

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